1. Technical Field
The present disclosure relates to presentation of product and service reviews, and, more specifically, to automatic generation of summaries of product and service reviews for presentation.
2. Related Art
Recent years have seen a dramatic increase in the use of networked mobile devices for daily life tasks such as finding and purchasing products and services. This kind of task involves search (to retrieve matching entities or businesses) and selection (of retrieved results). Selection decisions are increasingly made on the basis of surveying online reviews, which are abundantly available online. However, even from traditional PC interfaces, gathering information relevant to shoppers from product and service search results involves a significant amount of time reading reviews and weeding out extraneous information, and mobile users lack time and display space for leisurely perusal of hundreds (or thousands) of reviews for dozens of search results. While recent work in multi-document summarization has attempted to some degree to address this challenge, many questions about extracting and aggregating opinions remain unanswered.
Reviews can be described as textual opinions about a single product, e.g., consumer goods such as digital cameras, DVD players, or books; or about a service like lodging in a hotel or dining in a restaurant. Because the product or service can have several ratable aspects (sometimes referred to also as topics or features), any one review can provide evaluative text about one or more aspects. Accordingly, a review can be viewed as a set of aspects, each with an associated rating for a particular entity, where an entity refers to the particular type or brand of product, or particular restaurant, hotel, and so on providing services.
Ratings define the polarity (favorable, unfavorable, neutral) and strength of polarity (5 of 5 being strongly favorable, for example, 4 of 5 being weakly unfavorable, 3 of 5 being neutral, for example) of opinions and are preferably correlated with a range of integer values, which can be visualized as a number of symbols corresponding to the integer value. Typically, ratings are entered by reviewers as a number of stars corresponding to an integer number between 1 and 5, five (5) stars indicating a strong positive or most favorable opinion, one (1) star indicating a strong negative (unfavorable) opinion, and three (3) stars indicating a neutral opinion. The reviews can be entered, for example, by various authors, through a blog, a website, an app, and so-on, and retrieved according to one of various methods known in the art.
There are three main approaches that have been used to synthesize reviews. The first is to summarize the reviews graphically, while the other two produce textual review summaries. One method of forming a textural review is by extractive summarization. Current extractive summarization involves the selection and knitting together of text fragments from input reviews. The other is abstractive summarization, which involves the generation of new text sentences to express information about the range, polarity and strength of the opinions expressed in the input reviews. Neither type of textual summarization technique, however, currently produces user-targeted, concise, and reliable summary reviews.
The mobile device presents particular new challenges for both search and review browsing services:                Screen size—Mobile devices have relatively small displays and limited navigation capabilities.        Time—Mobile users are often on-the-move with limited time to refine search criteria or select relevant information from a long list of results and would benefit from information being presented that is targeted to the user's goals.        Location—Mobile users are highly focused on executing geographically local plans such as finding restaurants, entertainment events, or retail stores. Again, this presents a need for targeted results.        Personalization—For mobile users, personal data (e.g., search and purchasing histories) can be used to improve the precision of search results and the informativeness of reviews.        
Although constrained by the same factors, typically, mobile search and mobile review browsing are treated as different tasks using a combination of poorly integrated algorithms. This leads to inefficiencies and decreases user satisfaction.
For example, imagine that a consumer wants to buy a particular brand of shoes. The consumer would first use a local mobile search engine to find nearby shoe stores. The search engine might re-rank search results by using geographic information about the current user's location—or an explicitly requested location—and, optionally, re-score the final results based on domain knowledge and/or the user's search history. Once in the store, the user may use a separate internet search to find and browse online reviews and ratings for particular types of shoes. Opinion mining and sentiment analysis methods can be applied to extract the targets, and the relative polarity (e.g., positive, negative, or neutral) of the opinions expressed in the reviews. Lastly, the user must synthesize (or summarize) all the facts, opinions, and ratings by reading the reviews from the previous step to find the most desirable option, depending on the user's particular tastes.
While some methods exist for performing the first two steps in this process (search and sentiment analysis), there are no known methods for summarization of evaluative text of reviews (e.g., opinion or sentiment-laden text) in a targeted, concise, reliable, and readable form, particularly for use on a small mobile device.
For example, “Have2eat” is a popular restaurant search and reviewing application available for iPhone and Android-based devices. During a search, Have2eat uses geo-location information (from the GPS device or explicitly entered by the user) to produce a list of matching restaurants sorted by distance and located within a specific radius from the originating location. During browsing of search results, when restaurant reviews are available, a compact one-screen digest displays a listing of the reviews posted on the web by other customers. Customers can expand to read a full review page and can also enter their own ratings, comments and feedback. Review summaries can be visualized on the mobile screen in the form of:                a graphical summary by thumbs-up (positive reviews) and thumbs-down (negative reviews) for different aspects of the restaurant, where the summary finding is obtained by simply counting how many reviews were positive and how many were negative; and        textually by a few sentences selected from review texts that best summarize the opinions about various aspects of the restaurant expressed in the reviews.        
There are other similar applications commercially available for mobile platforms, however, most of them are only focused on the search task. When available, reviews, for example, restaurant reviews, are simply visualized as a contiguous list of text snippets with the overall experience rating.